Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies 2nd Edition

I have IT background as a Python Software Developer (1 year of experience) and nothing to do with mathematics(statistics, linear algebra, calculus,...) or machine learning related stuffs.I've only read through the first 3 chapter and i can say: This book is very easy to read and follow by each chapter. No mathematical background required. Extremely well organized and i think this book will provide a solid basic foundation for you to dig deeper into more complex problems using machine learning.The second edition also include a small section of "Unsupervised learning" which is very well writtenThere's a whole section of Appendixes for who are not familiar with some maths stuff. Also very well written.There's no code provided. Only theory and some formulas but it's easy to grasp if you read carefully and slowly. The theory is very well explained before dive deeper into complex parts. The pros and cons, variations of each algorithms are also discussed thorougly.Highly recommend for people who are:-Completely newbie: no background of anything required.-Want to learn its applications through case studies.-Want to know how a machine learning lifecycle looks like and other business requirements before applying machine learning to solve business's problem.-Grasp a solid foundation of machine learning models: how does this model work,when to use, how to use, other considerations ...-Or to expand further knowledge of WHAT,WHEN and HOW to use machine learning.This book will serve you well on your machine learning journey.Note: Most parts of this book is focusing on Predictive Analytics. And only a small part of Unsupervised learning and Deep learning is discussed.

I have read tons of machine learning books, but I have yet to read one where the authors have taken so care to motivate each topic and provide numerous calculations demonstrating how the algorithms work in practice. This book strikes a great balance between theory and practicality, and is by far the most accessible book I’ve read. The only thing I dislike is the notation, since it’s quite nonstandard, but that’s really quite small considering how great everything is.

Besides the book itself being excellent and one I would HIGHLY recommend (it is great in so many ways), the seller was excellent as well. The book arrived on time, was well packaged, and met all my expectations. Thank you for that!

I have been meaning to learn about this topic. This book is a gateway book. After reading I felt like I had the tools to dig deeper in any of the topics it covered. This was the book that started it all. I have been a journey since then. I loved the style of writing the chosen topics. The way the authors built up to neural networks was quite sensible. If you are looking to learn for this topic for the first time this is the book.

El contenido del libro comienza con una detallada definicion elemental de varios metodos y esta apoyado con presentaciones especialmente dedicadas a cada catitulo. Muy util para alumnos y educadores.

...Good overview of theory, nice, balanced 360 degree view, but a couple things would have helped:- A bit more brevity in exposition. Sometimes the pace is a bit slow.- Code examples for the concepts, so things can be tried 'hands on'.Nevertheless, I find myself often going back to this book to review the theory side of things.

I don't usually get much out of textbooks, but this one is straightforward, easy to follow, and provides thorough walkthroughs of sample problems. Highly recommended for anyone exploring this field.

Help me better understand Data Sciences.